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Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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使用基因组数据和机器学习来预测抗生素耐药性:一篇教程论文

Faye Orcales1,2, Lucy Moctezuma Tan1,3, Meris Johnson-Hagler1

  • 1Department of Biology, San Francisco State University, San Francisco, California, United States of America.

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概括
此摘要是机器生成的。

本研究介绍了一种机器学习 (ML) 教程,用于预测细菌的抗生素耐药性. 它培训学生使用ML工具进行更准确和更具成本效益的耐药性测试.

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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 抗生素耐药性是一个重大的全球健康威胁,需要先进的诊断工具.
  • 基因组测序与机器学习 (ML) 结合,有可能提高细菌耐药性检测的准确性和成本效益.

研究的目的:

  • 提供一个初学者友好的,逐步教程,用于培训和评估ML模型来预测抗生素耐药性.
  • 为健康前期和生命科学学生提供实践技能,以应用ML应对医疗挑战.

主要方法:

  • 该教程引导用户通过数据准备和训练四个ML模型:逻辑回归,随机森林,极端梯度增强树木和神经网络.
  • 模型性能使用各种指标和交叉验证技术进行评估.
  • 该教程在Google Colab笔记本中实现,不需要安装软件.

主要成果:

  • 该教程成功地展示了构建和评估ML模型的过程,以预测大肠杆菌的耐药性.
  • 它提供了对不同ML算法及其在微生物学中的应用的基本理解.

结论:

  • 机器学习为增强抗生素耐药性测试提供了一种有价值的方法.
  • 本教程作为一个可访问的教育资源,为学生学习必要的ML技能,以未来的医疗应用.